A system for context-dependent user modeling

Authors:
Petteri Nurmi;Alfons Salden;Sian Lun Lau;Jukka Suomela;Michael Sutterer;Jean Millerat;Miquel Martin;Eemil Lagerspetz;Remco Poortinga
Affiliations:
Helsinki Institute for Information Technology HIIT, University of Helsinki, Finland;Telematica Instituut (TELIN), Enschede, AN, The Netherlands;Faculty of Electrical Engineering, University of Kassel, Kassel, Germany;Helsinki Institute for Information Technology HIIT, University of Helsinki, Finland;Faculty of Electrical Engineering, University of Kassel, Kassel, Germany;Motorola Labs, Parc Les Algorithmes, Saint-Aubin, Gif-sur-Yvette Cedex, France;NEC Europe Ltd, Heidelberg, Germany;Helsinki Institute for Information Technology HIIT, University of Helsinki, Finland;Telematica Instituut (TELIN), Enschede, AN, The Netherlands
Venue:
OTM'06 Proceedings of the 2006 international conference on On the Move to Meaningful Internet Systems: AWeSOMe, CAMS, COMINF, IS, KSinBIT, MIOS-CIAO, MONET - Volume Part II
Year:
2006

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Abstract

We present a system for learning and utilizing context-dependent user models The user models attempt to capture the interests of a user and link the interests to the situation of the user The models are used for making recommendations to applications and services on what might interest the user in her current situation In the design process we have analyzed several mock-ups of new mobile, context-aware services and applications The mock-ups spanned rather diverse domains, which helped us to ensure that the system is applicable to a wide range of tasks, such as modality recommendations (e.g., switching to speech output when driving a car), service category recommendations (e.g., journey planners at a bus stop), and recommendations of group members (e.g., people with whom to share a car) The structure of the presented system is highly modular First of all, this ensures that the algorithms that are used to build the user models can be easily replaced Secondly, the modularity makes it easier to evaluate how well different algorithms perform in different domains The current implementation of the system supports rule based reasoning and tree augmented naïve Bayesian classifiers (TAN) The system consists of three components, each of which has been implemented as a web service The entire system has been deployed and is in use in the EU IST project MobiLife In this paper, we detail the components that are part of the system and introduce the interactions between the components In addition, we briefly discuss the quality of the recommendations that our system produces.